Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

ANÁLISE QUALI-QUANTITATIVA DOS RESÍDUOS GERADOS EM ENFERMARIA DO HOSPITAL SANTA CASA DE MISERICÓRDIA DE VITÓRIA, ESPÍRITO SANTO

The qualitative and quantitative analysis of solid waste of health services allows the identification of opportunities to minimize their generation by recycling some of its components, reducing the total mass of infectious waste, identifying compliance concerning segregation at source, among others. This study aimed to investigate qualitative and quantitatively the waste generated in the Santa Luiza Infirmary from Hospital Santa Casa de Misericordia de Vitoria - ES, and evaluate the waste management in accordance with ANVISA Resolution No. 306/2004. A comparison between the current scenario and an ideal scenario of segregation, and estimated the reduction in treatment and final disposal costs. It was concluded that most of the waste generated in the infirmary is residue Group D (about 70%), i.e., common waste, reusable and recycling. Segregation was observed that about 61% of Group D were mixed with waste residue of group A (infectious). It is possible to optimize these values through continuing education and professional training, as well as adoption of recycling practices thereby reducing the costs of treatment and final disposal.A análise quali-quantitativa dos resíduos sólidos de serviços de saúde permite a identificação de possibilidades para minimizar a sua geração através da reciclagem de alguns de seus componentes, a redução da massa total de resíduos infectantes, a identificação de inadequações quanto à segregação na fonte, entre outros. Este trabalho teve por objetivo analisar quali-quantitativamente os resíduos gerados na Enfermaria Santa Luiza do Hospital Santa Casa de Misericórdia de Vitória - ES, bem como avaliar o seu sistema de gerenciamento de acordo com a Resolução Anvisa nº 306/2004. Com base nesses dados, foi elaborada uma comparação entre o cenário atual e um cenário ideal de segregação e estimada a redução nos custos de tratamento e disposição final. Concluiu-se que grande parte dos resíduos gerados na enfermaria é resíduo Grupo D (cerca de 70%), ou seja, resíduos comuns, passíveis de reutilização e reciclagem. Foi observado que, na segregação, cerca de 61% de resíduos do Grupo D estavam misturados com resíduos do Grupo A (infectantes). É possível otimizar esses valores por meio de educação continuada e capacitação profissional, bem como adoção de práticas de reciclagem, reduzindo, assim, os custos com tratamento e disposição final.